Introduction to GPU Computing

Size: px
Start display at page:

Download "Introduction to GPU Computing"

Transcription

1 Matthis Hauschild Universität Hamburg Fakultät für Mathematik, Informatik und Naturwissenschaften Technische Aspekte Multimodaler Systeme December 4, 2014 M. Hauschild - 1

2 Table of Contents 1. Architecture of a GPU 2. General-purpose computing on GPUs 3. Applications of GPGPU 4. Performance evaluation examples M. Hauschild - 2

3 Architecture of a GPU What is a GPU Graphics processing unit Main GPU manufacturers 1. Intel 2. AMD 3. Nvidia Performance characteristics: 1 GPU architecture: 28 nm GPU speed: 1 GHz Memory amount: 8 GiB GDDR5 Memory bandwidth: 640 GiB/s 1 based on the AMD Radeon R9 series (cf.[1]) M. Hauschild - 3

4 Architecture of a GPU Difference between GPU and CPU[3] CPU optimized for single thread execution GPU optimized for multiple data execution M. Hauschild - 4

5 Architecture of a GPU Architecture of a GPU[4] based on the Nvidia Fermi architecture: M. Hauschild - 5

6 Architecture of a GPU Architecture of a GPU[4] M. Hauschild - 6

7 Architecture of a GPU Architecture of a GPU[4] Summary of the Nvidia Fermi architecture: 16 Streaming Multiprocessors (SM) 32 CUDA cores per SM = 512 CUDA cores 512 FMA op/clock it is great for generating graphics, but what else could be done with it? M. Hauschild - 7

8 General-purpose computing on GPUs What is GPGPU[5] General-purpose computing on graphics processing units Using GPU for non-graphical computations Good for data parallelism Bad for instruction parallelism First use in LU factorization Became popular at 2001 with matrix multiplication Started using DirectX and OpenGL M. Hauschild - 8

9 General-purpose computing on GPUs GPGPU Frameworks Brook One of the earliest GPU frameworks by Stanford University CUDA Proprietary Nvidia-only framework OpenCL Open source general framework by Khronos Group C++ AMP Open C++ extension by Microsoft OpenACC C, C++ and Fortran extension ArrayFire Wrapper for CUDA, OpenCL, etc. M. Hauschild - 9

10 Applications of GPGPU General applications of GPGPU Again, GPGPU can only be superior to CPU computing, if the same algorithm is applied to a lot of data (data parallelism) For example: k-nearest neighbor Fast Fourier Transform Segmentation Audio Processing CT reconstruction Weather forecasting Cryptography Database operations M. Hauschild - 10

11 Applications of GPGPU Applications of GPGPU in Robotics[2] For example: Generally many image processing tasks Frame transformation Inverse kinematic calculation 3D pose estimation Point-set registration M. Hauschild - 11

12 Universität Hamburg Performance evaluation examples Performance evaluation examples Test 1 Sobel operator on a real image using OpenCL Measurement of the possible frames per second On GPU and CPU Test 2 Matrix multiplication of two squared matrices using OpenCL Measurement of time needed for calculation On GPU and CPU M. Hauschild - 12

13 Performance evaluation examples Performance evaluation examples - System characteristics My CPU: Model: AMD Phenom II X4 965 Clock speed: 3400 MHz Misc: 4 Cores, SSE3 My GPU: Model: AMD Radeon HD 6950, Memory: 2048 MB Core clock: 800 MHz Memory clock: 1250 MHz Memory bandwidth: 160 GB/s My RAM: 8 GB M. Hauschild - 13

14 Performance evaluation examples Performance evaluation examples - Test 1 The Sobel operator: 3. s = dx 2 + dy 2 M. Hauschild - 14

15 Performance evaluation examples M. Hauschild - 15

16 Performance evaluation examples Performance evaluation examples - Test 1 M. Hauschild - 16

17 Performance evaluation examples Performance evaluation examples - Test 2 Matrix Multiplication 2 : 2 from M. Hauschild - 17

18 Performance evaluation examples Performance evaluation examples - Test 2 M. Hauschild - 18

19 Performance evaluation examples Thank you for your attention! Matthis Hauschild Universität Hamburg Fakultät für Mathematik, Informatik und Naturwissenschaften Technische Aspekte Multimodaler Systeme M. Hauschild - 19

20 Performance evaluation examples Bibliography [1] AMD. AMD Radeon TM R9 Grafikkartenserie, [2] J. Bedkowski and A. Maslowski. GPGPU computation in mobile robot applications. Warsaw University of Technology, [3] Nvidia. CUDA C Programming Guide, [4] Nvidia. NVIDIA s Next Generation CUDA Compute Architecture: Fermi, NVIDIA_Fermi_Compute_Architecture_Whitepaper.pdf. [5] Wikipedia. General-purpose computing on graphics processing units, graphics_processing_units. M. Hauschild - 20

Graphics Cards and Graphics Processing Units. Ben Johnstone Russ Martin November 15, 2011

Graphics Cards and Graphics Processing Units. Ben Johnstone Russ Martin November 15, 2011 Graphics Cards and Graphics Processing Units Ben Johnstone Russ Martin November 15, 2011 Contents Graphics Processing Units (GPUs) Graphics Pipeline Architectures 8800-GTX200 Fermi Cayman Performance Analysis

More information

GPU System Architecture. Alan Gray EPCC The University of Edinburgh

GPU System Architecture. Alan Gray EPCC The University of Edinburgh GPU System Architecture EPCC The University of Edinburgh Outline Why do we want/need accelerators such as GPUs? GPU-CPU comparison Architectural reasons for GPU performance advantages GPU accelerated systems

More information

Introduction to GP-GPUs. Advanced Computer Architectures, Cristina Silvano, Politecnico di Milano 1

Introduction to GP-GPUs. Advanced Computer Architectures, Cristina Silvano, Politecnico di Milano 1 Introduction to GP-GPUs Advanced Computer Architectures, Cristina Silvano, Politecnico di Milano 1 GPU Architectures: How do we reach here? NVIDIA Fermi, 512 Processing Elements (PEs) 2 What Can It Do?

More information

GPUs: Doing More Than Just Games. Mark Gahagan CSE 141 November 29, 2012

GPUs: Doing More Than Just Games. Mark Gahagan CSE 141 November 29, 2012 GPUs: Doing More Than Just Games Mark Gahagan CSE 141 November 29, 2012 Outline Introduction: Why multicore at all? Background: What is a GPU? Quick Look: Warps and Threads (SIMD) NVIDIA Tesla: The First

More information

Introduction to GPGPU. Tiziano Diamanti t.diamanti@cineca.it

Introduction to GPGPU. Tiziano Diamanti t.diamanti@cineca.it t.diamanti@cineca.it Agenda From GPUs to GPGPUs GPGPU architecture CUDA programming model Perspective projection Vectors that connect the vanishing point to every point of the 3D model will intersecate

More information

Graphics Processing Unit (GPU) Memory Hierarchy. Presented by Vu Dinh and Donald MacIntyre

Graphics Processing Unit (GPU) Memory Hierarchy. Presented by Vu Dinh and Donald MacIntyre Graphics Processing Unit (GPU) Memory Hierarchy Presented by Vu Dinh and Donald MacIntyre 1 Agenda Introduction to Graphics Processing CPU Memory Hierarchy GPU Memory Hierarchy GPU Architecture Comparison

More information

Introducing PgOpenCL A New PostgreSQL Procedural Language Unlocking the Power of the GPU! By Tim Child

Introducing PgOpenCL A New PostgreSQL Procedural Language Unlocking the Power of the GPU! By Tim Child Introducing A New PostgreSQL Procedural Language Unlocking the Power of the GPU! By Tim Child Bio Tim Child 35 years experience of software development Formerly VP Oracle Corporation VP BEA Systems Inc.

More information

Introduction GPU Hardware GPU Computing Today GPU Computing Example Outlook Summary. GPU Computing. Numerical Simulation - from Models to Software

Introduction GPU Hardware GPU Computing Today GPU Computing Example Outlook Summary. GPU Computing. Numerical Simulation - from Models to Software GPU Computing Numerical Simulation - from Models to Software Andreas Barthels JASS 2009, Course 2, St. Petersburg, Russia Prof. Dr. Sergey Y. Slavyanov St. Petersburg State University Prof. Dr. Thomas

More information

Introduction to GPU Programming Languages

Introduction to GPU Programming Languages CSC 391/691: GPU Programming Fall 2011 Introduction to GPU Programming Languages Copyright 2011 Samuel S. Cho http://www.umiacs.umd.edu/ research/gpu/facilities.html Maryland CPU/GPU Cluster Infrastructure

More information

NVIDIA GeForce GTX 580 GPU Datasheet

NVIDIA GeForce GTX 580 GPU Datasheet NVIDIA GeForce GTX 580 GPU Datasheet NVIDIA GeForce GTX 580 GPU Datasheet 3D Graphics Full Microsoft DirectX 11 Shader Model 5.0 support: o NVIDIA PolyMorph Engine with distributed HW tessellation engines

More information

Next Generation GPU Architecture Code-named Fermi

Next Generation GPU Architecture Code-named Fermi Next Generation GPU Architecture Code-named Fermi The Soul of a Supercomputer in the Body of a GPU Why is NVIDIA at Super Computing? Graphics is a throughput problem paint every pixel within frame time

More information

GPU Architectures. A CPU Perspective. Data Parallelism: What is it, and how to exploit it? Workload characteristics

GPU Architectures. A CPU Perspective. Data Parallelism: What is it, and how to exploit it? Workload characteristics GPU Architectures A CPU Perspective Derek Hower AMD Research 5/21/2013 Goals Data Parallelism: What is it, and how to exploit it? Workload characteristics Execution Models / GPU Architectures MIMD (SPMD),

More information

Comparing CPU and GPU in OLAP Cube Creation

Comparing CPU and GPU in OLAP Cube Creation Comparing CPU and GPU in OLAP Cube Creation SOFSEM 2011 Krzysztof Kaczmarski Faculty of Mathematics and Information Science Warsaw University of Technology 22-28 January 2011 Outline 1 Introduction Introduction

More information

Parallel Programming Survey

Parallel Programming Survey Christian Terboven 02.09.2014 / Aachen, Germany Stand: 26.08.2014 Version 2.3 IT Center der RWTH Aachen University Agenda Overview: Processor Microarchitecture Shared-Memory

More information

Introduction to GPU hardware and to CUDA

Introduction to GPU hardware and to CUDA Introduction to GPU hardware and to CUDA Philip Blakely Laboratory for Scientific Computing, University of Cambridge Philip Blakely (LSC) GPU introduction 1 / 37 Course outline Introduction to GPU hardware

More information

HP Workstations graphics card options

HP Workstations graphics card options Family data sheet HP Workstations graphics card options Quick reference guide Leading-edge professional graphics February 2013 A full range of graphics cards to meet your performance needs compare features

More information

RWTH GPU Cluster. Sandra Wienke wienke@rz.rwth-aachen.de November 2012. Rechen- und Kommunikationszentrum (RZ) Fotos: Christian Iwainsky

RWTH GPU Cluster. Sandra Wienke wienke@rz.rwth-aachen.de November 2012. Rechen- und Kommunikationszentrum (RZ) Fotos: Christian Iwainsky RWTH GPU Cluster Fotos: Christian Iwainsky Sandra Wienke wienke@rz.rwth-aachen.de November 2012 Rechen- und Kommunikationszentrum (RZ) The RWTH GPU Cluster GPU Cluster: 57 Nvidia Quadro 6000 (Fermi) innovative

More information

Medical Image Processing on the GPU. Past, Present and Future. Anders Eklund, PhD Virginia Tech Carilion Research Institute andek@vtc.vt.

Medical Image Processing on the GPU. Past, Present and Future. Anders Eklund, PhD Virginia Tech Carilion Research Institute andek@vtc.vt. Medical Image Processing on the GPU Past, Present and Future Anders Eklund, PhD Virginia Tech Carilion Research Institute andek@vtc.vt.edu Outline Motivation why do we need GPUs? Past - how was GPU programming

More information

L20: GPU Architecture and Models

L20: GPU Architecture and Models L20: GPU Architecture and Models scribe(s): Abdul Khalifa 20.1 Overview GPUs (Graphics Processing Units) are large parallel structure of processing cores capable of rendering graphics efficiently on displays.

More information

Analysis of GPU Parallel Computing based on Matlab

Analysis of GPU Parallel Computing based on Matlab Analysis of GPU Parallel Computing based on Matlab Mingzhe Wang, Bo Wang, Qiu He, Xiuxiu Liu, Kunshuai Zhu (School of Computer and Control Engineering, University of Chinese Academy of Sciences, Huairou,

More information

HIGH PERFORMANCE CONSULTING COURSE OFFERINGS

HIGH PERFORMANCE CONSULTING COURSE OFFERINGS Performance 1(6) HIGH PERFORMANCE CONSULTING COURSE OFFERINGS LEARN TO TAKE ADVANTAGE OF POWERFUL GPU BASED ACCELERATOR TECHNOLOGY TODAY 2006 2013 Nvidia GPUs Intel CPUs CONTENTS Acronyms and Terminology...

More information

~ Greetings from WSU CAPPLab ~

~ Greetings from WSU CAPPLab ~ ~ Greetings from WSU CAPPLab ~ Multicore with SMT/GPGPU provides the ultimate performance; at WSU CAPPLab, we can help! Dr. Abu Asaduzzaman, Assistant Professor and Director Wichita State University (WSU)

More information

GPU Architecture. Michael Doggett ATI

GPU Architecture. Michael Doggett ATI GPU Architecture Michael Doggett ATI GPU Architecture RADEON X1800/X1900 Microsoft s XBOX360 Xenos GPU GPU research areas ATI - Driving the Visual Experience Everywhere Products from cell phones to super

More information

SAPPHIRE R9 270X 4GB GDDR5 WITH BOOST & OC

SAPPHIRE R9 270X 4GB GDDR5 WITH BOOST & OC SAPPHIRE R9 270X 4GB GDDR5 WITH BOOST & OC Specification Display Support Output GPU Video Memory Dimension Software Accessory 3 x Maximum Display Monitor(s) support 1 x HDMI (with 3D) 1 x DisplayPort 1.2

More information

QCD as a Video Game?

QCD as a Video Game? QCD as a Video Game? Sándor D. Katz Eötvös University Budapest in collaboration with Győző Egri, Zoltán Fodor, Christian Hoelbling Dániel Nógrádi, Kálmán Szabó Outline 1. Introduction 2. GPU architecture

More information

HP Workstations graphics card options

HP Workstations graphics card options Family data sheet HP Workstations graphics card options Quick reference guide Leading-edge professional graphics March 2014 A full range of graphics cards to meet your performance needs compare features

More information

Appendix L. General-purpose GPU Radiative Solver. Andrea Tosetto Marco Giardino Matteo Gorlani (Blue Engineering & Design, Italy)

Appendix L. General-purpose GPU Radiative Solver. Andrea Tosetto Marco Giardino Matteo Gorlani (Blue Engineering & Design, Italy) 141 Appendix L General-purpose GPU Radiative Solver Andrea Tosetto Marco Giardino Matteo Gorlani (Blue Engineering & Design, Italy) 14 15 October 2014 142 General-purpose GPU Radiative Solver Abstract

More information

Programming models for heterogeneous computing. Manuel Ujaldón Nvidia CUDA Fellow and A/Prof. Computer Architecture Department University of Malaga

Programming models for heterogeneous computing. Manuel Ujaldón Nvidia CUDA Fellow and A/Prof. Computer Architecture Department University of Malaga Programming models for heterogeneous computing Manuel Ujaldón Nvidia CUDA Fellow and A/Prof. Computer Architecture Department University of Malaga Talk outline [30 slides] 1. Introduction [5 slides] 2.

More information

Introduction to GPU Architecture

Introduction to GPU Architecture Introduction to GPU Architecture Ofer Rosenberg, PMTS SW, OpenCL Dev. Team AMD Based on From Shader Code to a Teraflop: How GPU Shader Cores Work, By Kayvon Fatahalian, Stanford University Content 1. Three

More information

GPGPU Computing. Yong Cao

GPGPU Computing. Yong Cao GPGPU Computing Yong Cao Why Graphics Card? It s powerful! A quiet trend Copyright 2009 by Yong Cao Why Graphics Card? It s powerful! Processor Processing Units FLOPs per Unit Clock Speed Processing Power

More information

The High Performance Internet of Things: using GVirtuS for gluing cloud computing and ubiquitous connected devices

The High Performance Internet of Things: using GVirtuS for gluing cloud computing and ubiquitous connected devices WS on Models, Algorithms and Methodologies for Hierarchical Parallelism in new HPC Systems The High Performance Internet of Things: using GVirtuS for gluing cloud computing and ubiquitous connected devices

More information

INF5063: Programming heterogeneous multi-core processors. September 13, 2010

INF5063: Programming heterogeneous multi-core processors. September 13, 2010 INF5063: Programming heterogeneous multi-core processors September 13, 2010 Overview Course topic and scope Background for the use and parallel processing using heterogeneous multi-core processors Examples

More information

NVIDIA GeForce Experience

NVIDIA GeForce Experience NVIDIA GeForce Experience DU-05620-001_v02 October 9, 2012 User Guide TABLE OF CONTENTS 1 NVIDIA GeForce Experience User Guide... 1 About GeForce Experience... 1 Installing and Setting Up GeForce Experience...

More information

An OpenCL Candidate Slicing Frequent Pattern Mining Algorithm on Graphic Processing Units*

An OpenCL Candidate Slicing Frequent Pattern Mining Algorithm on Graphic Processing Units* An OpenCL Candidate Slicing Frequent Pattern Mining Algorithm on Graphic Processing Units* Che-Yu Lin Science and Information Engineering Chung Hua University b09502017@chu.edu.tw Kun-Ming Yu Science and

More information

HP Z Workstations graphics card options

HP Z Workstations graphics card options Sales guide HP Z Workstations graphics card options Quick reference guide Table of contents Desktop Workstations compatibility... 3 Mobile and All-in-One Workstations compatibility... 4 Compare features:

More information

SAPPHIRE TOXIC R9 270X 2GB GDDR5 WITH BOOST

SAPPHIRE TOXIC R9 270X 2GB GDDR5 WITH BOOST SAPPHIRE TOXIC R9 270X 2GB GDDR5 WITH BOOST Specification Display Support Output GPU Video Memory Dimension Software Accessory supports up to 4 display monitor(s) without DisplayPort 4 x Maximum Display

More information

ACCELERATING SELECT WHERE AND SELECT JOIN QUERIES ON A GPU

ACCELERATING SELECT WHERE AND SELECT JOIN QUERIES ON A GPU Computer Science 14 (2) 2013 http://dx.doi.org/10.7494/csci.2013.14.2.243 Marcin Pietroń Pawe l Russek Kazimierz Wiatr ACCELERATING SELECT WHERE AND SELECT JOIN QUERIES ON A GPU Abstract This paper presents

More information

GPU programming using C++ AMP

GPU programming using C++ AMP GPU programming using C++ AMP Petrika Manika petrika.manika@fshn.edu.al Elda Xhumari elda.xhumari@fshn.edu.al Julian Fejzaj julian.fejzaj@fshn.edu.al Abstract Nowadays, a challenge for programmers is to

More information

GPGPU for Real-Time Data Analytics: Introduction. Nanyang Technological University, Singapore 2

GPGPU for Real-Time Data Analytics: Introduction. Nanyang Technological University, Singapore 2 GPGPU for Real-Time Data Analytics: Introduction Bingsheng He 1, Huynh Phung Huynh 2, Rick Siow Mong Goh 2 1 Nanyang Technological University, Singapore 2 A*STAR Institute of High Performance Computing,

More information

Building Blocks. CPUs, Memory and Accelerators

Building Blocks. CPUs, Memory and Accelerators Building Blocks CPUs, Memory and Accelerators Outline Computer layout CPU and Memory What does performance depend on? Limits to performance Silicon-level parallelism Single Instruction Multiple Data (SIMD/Vector)

More information

Transcend the Vision. Embedded Graphic Solutions that Lead to New Territory. Embedded Graphic Solutions. www.advantech.com

Transcend the Vision. Embedded Graphic Solutions that Lead to New Territory. Embedded Graphic Solutions. www.advantech.com Transcend the Vision Embedded Graphic Solutions that Lead to New Territory Embedded Graphic Solutions www.advantech.com Compact Product Portfolio Designed for Compatibility One-slot Design Low Profile

More information

Evaluation of CUDA Fortran for the CFD code Strukti

Evaluation of CUDA Fortran for the CFD code Strukti Evaluation of CUDA Fortran for the CFD code Strukti Practical term report from Stephan Soller High performance computing center Stuttgart 1 Stuttgart Media University 2 High performance computing center

More information

Clustering Billions of Data Points Using GPUs

Clustering Billions of Data Points Using GPUs Clustering Billions of Data Points Using GPUs Ren Wu ren.wu@hp.com Bin Zhang bin.zhang2@hp.com Meichun Hsu meichun.hsu@hp.com ABSTRACT In this paper, we report our research on using GPUs to accelerate

More information

SAPPHIRE VAPOR-X R9 270X 2GB GDDR5 OC WITH BOOST

SAPPHIRE VAPOR-X R9 270X 2GB GDDR5 OC WITH BOOST SAPPHIRE VAPOR-X R9 270X 2GB GDDR5 OC WITH BOOST Specification Display Support Output GPU Video Memory Dimension Software Accessory 4 x Maximum Display Monitor(s) support 1 x HDMI (with 3D) 1 x DisplayPort

More information

Case Study on Productivity and Performance of GPGPUs

Case Study on Productivity and Performance of GPGPUs Case Study on Productivity and Performance of GPGPUs Sandra Wienke wienke@rz.rwth-aachen.de ZKI Arbeitskreis Supercomputing April 2012 Rechen- und Kommunikationszentrum (RZ) RWTH GPU-Cluster 56 Nvidia

More information

IP Video Rendering Basics

IP Video Rendering Basics CohuHD offers a broad line of High Definition network based cameras, positioning systems and VMS solutions designed for the performance requirements associated with critical infrastructure applications.

More information

Program Optimization Study on a 128-Core GPU

Program Optimization Study on a 128-Core GPU Program Optimization Study on a 128-Core GPU Shane Ryoo, Christopher I. Rodrigues, Sam S. Stone, Sara S. Baghsorkhi, Sain-Zee Ueng, and Wen-mei W. Hwu Yu, Xuan Dept of Computer & Information Sciences University

More information

Video Conferencing System Requirements

Video Conferencing System Requirements Video Conferencing System Requirements TrueConf system and network requirements depend on chosen video conferencing mode and applied video quality. Video resolution and frame rate are selected automatically

More information

Sudoku Solution on Many-core Processors

Sudoku Solution on Many-core Processors Acceleration of Genetic Algorithms for Sudoku Solution on Many-core Processors Yuji Sato* 1, Naohiro Hasegawa* 1, Mikiko Sato* 2 * 1 : Hosei University, * 2 : Tokyo University of A&T 0 Outline Background

More information

ATI Radeon 4800 series Graphics. Michael Doggett Graphics Architecture Group Graphics Product Group

ATI Radeon 4800 series Graphics. Michael Doggett Graphics Architecture Group Graphics Product Group ATI Radeon 4800 series Graphics Michael Doggett Graphics Architecture Group Graphics Product Group Graphics Processing Units ATI Radeon HD 4870 AMD Stream Computing Next Generation GPUs 2 Radeon 4800 series

More information

GPU Architecture Overview. John Owens UC Davis

GPU Architecture Overview. John Owens UC Davis GPU Architecture Overview John Owens UC Davis The Right-Hand Turn [H&P Figure 1.1] Why? [Architecture Reasons] ILP increasingly difficult to extract from instruction stream Control hardware dominates µprocessors

More information

Parallel Image Processing with CUDA A case study with the Canny Edge Detection Filter

Parallel Image Processing with CUDA A case study with the Canny Edge Detection Filter Parallel Image Processing with CUDA A case study with the Canny Edge Detection Filter Daniel Weingaertner Informatics Department Federal University of Paraná - Brazil Hochschule Regensburg 02.05.2011 Daniel

More information

Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data

Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data Amanda O Connor, Bryan Justice, and A. Thomas Harris IN52A. Big Data in the Geosciences:

More information

Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data

Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data Amanda O Connor, Bryan Justice, and A. Thomas Harris IN52A. Big Data in the Geosciences:

More information

LBM BASED FLOW SIMULATION USING GPU COMPUTING PROCESSOR

LBM BASED FLOW SIMULATION USING GPU COMPUTING PROCESSOR LBM BASED FLOW SIMULATION USING GPU COMPUTING PROCESSOR Frédéric Kuznik, frederic.kuznik@insa lyon.fr 1 Framework Introduction Hardware architecture CUDA overview Implementation details A simple case:

More information

¹ Autodesk Showcase 2016 and Autodesk ReCap 2016 are not supported in 32-Bit.

¹ Autodesk Showcase 2016 and Autodesk ReCap 2016 are not supported in 32-Bit. Autodesk Factory Design Suite Standard 2016 Supported OS 32-Bit OS ¹: Microsoft Windows 7 Home Premium, Professional, Ultimate, Enterprise Microsoft Windows 8/8.1, Pro, Enterprise² 64-bit OS: Microsoft

More information

System requirements for Autodesk Building Design Suite 2017

System requirements for Autodesk Building Design Suite 2017 System requirements for Autodesk Building Design Suite 2017 For specific recommendations for a product within the Building Design Suite, please refer to that products system requirements for additional

More information

QuickSpecs. NVIDIA Quadro M6000 12GB Graphics INTRODUCTION. NVIDIA Quadro M6000 12GB Graphics. Overview

QuickSpecs. NVIDIA Quadro M6000 12GB Graphics INTRODUCTION. NVIDIA Quadro M6000 12GB Graphics. Overview Overview L2K02AA INTRODUCTION Push the frontier of graphics processing with the new NVIDIA Quadro M6000 12GB graphics card. The Quadro M6000 features the top of the line member of the latest NVIDIA Maxwell-based

More information

ultra fast SOM using CUDA

ultra fast SOM using CUDA ultra fast SOM using CUDA SOM (Self-Organizing Map) is one of the most popular artificial neural network algorithms in the unsupervised learning category. Sijo Mathew Preetha Joy Sibi Rajendra Manoj A

More information

NVIDIA Quadro K2200. Product Specifications. NVIDIA Quadro K2200 Part No. VCQK2200 PB $ CUDA Cores 640. Maximum Power Consumption

NVIDIA Quadro K2200. Product Specifications. NVIDIA Quadro K2200 Part No. VCQK2200 PB $ CUDA Cores 640. Maximum Power Consumption NVIDIA Quadro K2200 NVIDIA Quadro K2200 Part No. VCQK2200 PB $599.00 84 0 0 36 Product Specifications CUDA Cores 640 GPU Memory Memory Interface Memory Bandwidth System Interface Maximum Power Consumption

More information

Applying Parallel and Distributed Computing for Image Reconstruction in 3D Electrical Capacitance Tomography

Applying Parallel and Distributed Computing for Image Reconstruction in 3D Electrical Capacitance Tomography AUTOMATYKA 2010 Tom 14 Zeszyt 3/2 Pawe³ Kapusta*, Micha³ Majchrowicz*, Robert Banasiak* Applying Parallel and Distributed Computing for Image Reconstruction in 3D Electrical Capacitance Tomography 1. Introduction

More information

Lecture 11: Multi-Core and GPU. Multithreading. Integration of multiple processor cores on a single chip.

Lecture 11: Multi-Core and GPU. Multithreading. Integration of multiple processor cores on a single chip. Lecture 11: Multi-Core and GPU Multi-core computers Multithreading GPUs General Purpose GPUs Zebo Peng, IDA, LiTH 1 Multi-Core System Integration of multiple processor cores on a single chip. To provide

More information

Z440 Management Workstation

Z440 Management Workstation Z440 Management Workstation Supported Graphic Cards en Technical Service Note Z440 Management Workstation Table of Contents en 3 Table of contents 1 Introduction 4 2 NVIDIA Quadro K620 2GB Graphics 5

More information

GPU for Scientific Computing. -Ali Saleh

GPU for Scientific Computing. -Ali Saleh 1 GPU for Scientific Computing -Ali Saleh Contents Introduction What is GPU GPU for Scientific Computing K-Means Clustering K-nearest Neighbours When to use GPU and when not Commercial Programming GPU

More information

AMD EMBEDDED PCIe ADD-IN BOARD Comparison

AMD EMBEDDED PCIe ADD-IN BOARD Comparison AMD EMBEDDED PCIe ADD-IN BOARD Comparison AMD Radeon E6460 AMD Radeon E6760 Graphics Processing Unit Process Technology 40 nm 40 nm Graphics Engine Operating Frequency (max) 600 MHz 600 MHz CPU Interface

More information

Computer Graphics Hardware An Overview

Computer Graphics Hardware An Overview Computer Graphics Hardware An Overview Graphics System Monitor Input devices CPU/Memory GPU Raster Graphics System Raster: An array of picture elements Based on raster-scan TV technology The screen (and

More information

System Requirements. Autodesk Building Design Suite Standard 2013

System Requirements. Autodesk Building Design Suite Standard 2013 There are separate system requirements for each of the three different editions of the Autodesk Building Design Suite. Please make sure that you are referencing the appropriate section of this document

More information

Introduction to Numerical General Purpose GPU Computing with NVIDIA CUDA. Part 1: Hardware design and programming model

Introduction to Numerical General Purpose GPU Computing with NVIDIA CUDA. Part 1: Hardware design and programming model Introduction to Numerical General Purpose GPU Computing with NVIDIA CUDA Part 1: Hardware design and programming model Amin Safi Faculty of Mathematics, TU dortmund January 22, 2016 Table of Contents Set

More information

ArcGIS Pro: Virtualizing in Citrix XenApp and XenDesktop. Emily Apsey Performance Engineer

ArcGIS Pro: Virtualizing in Citrix XenApp and XenDesktop. Emily Apsey Performance Engineer ArcGIS Pro: Virtualizing in Citrix XenApp and XenDesktop Emily Apsey Performance Engineer Presentation Overview What it takes to successfully virtualize ArcGIS Pro in Citrix XenApp and XenDesktop - Shareable

More information

GPU-BASED TUNING OF QUANTUM-INSPIRED GENETIC ALGORITHM FOR A COMBINATORIAL OPTIMIZATION PROBLEM

GPU-BASED TUNING OF QUANTUM-INSPIRED GENETIC ALGORITHM FOR A COMBINATORIAL OPTIMIZATION PROBLEM GPU-BASED TUNING OF QUANTUM-INSPIRED GENETIC ALGORITHM FOR A COMBINATORIAL OPTIMIZATION PROBLEM Robert Nowotniak, Jacek Kucharski Computer Engineering Department The Faculty of Electrical, Electronic,

More information

Parallel Firewalls on General-Purpose Graphics Processing Units

Parallel Firewalls on General-Purpose Graphics Processing Units Parallel Firewalls on General-Purpose Graphics Processing Units Manoj Singh Gaur and Vijay Laxmi Kamal Chandra Reddy, Ankit Tharwani, Ch.Vamshi Krishna, Lakshminarayanan.V Department of Computer Engineering

More information

Accelerating Intensity Layer Based Pencil Filter Algorithm using CUDA

Accelerating Intensity Layer Based Pencil Filter Algorithm using CUDA Accelerating Intensity Layer Based Pencil Filter Algorithm using CUDA Dissertation submitted in partial fulfillment of the requirements for the degree of Master of Technology, Computer Engineering by Amol

More information

GPGPU accelerated Computational Fluid Dynamics

GPGPU accelerated Computational Fluid Dynamics t e c h n i s c h e u n i v e r s i t ä t b r a u n s c h w e i g Carl-Friedrich Gauß Faculty GPGPU accelerated Computational Fluid Dynamics 5th GACM Colloquium on Computational Mechanics Hamburg Institute

More information

Turbomachinery CFD on many-core platforms experiences and strategies

Turbomachinery CFD on many-core platforms experiences and strategies Turbomachinery CFD on many-core platforms experiences and strategies Graham Pullan Whittle Laboratory, Department of Engineering, University of Cambridge MUSAF Colloquium, CERFACS, Toulouse September 27-29

More information

Alberto Corrales-García, Rafael Rodríguez-Sánchez, José Luis Martínez, Gerardo Fernández-Escribano, José M. Claver and José Luis Sánchez

Alberto Corrales-García, Rafael Rodríguez-Sánchez, José Luis Martínez, Gerardo Fernández-Escribano, José M. Claver and José Luis Sánchez Alberto Corrales-García, Rafael Rodríguez-Sánchez, José Luis artínez, Gerardo Fernández-Escribano, José. Claver and José Luis Sánchez 1. Introduction 2. Technical Background 3. Proposed DVC to H.264/AVC

More information

INF5063: Programming heterogeneous multi-core processors Introduction

INF5063: Programming heterogeneous multi-core processors Introduction INF5063: Programming heterogeneous multi-core processors Introduction 28/8-2009 Overview Course topic and scope Background for the use and parallel processing using heterogeneous multi-core processors

More information

Accelerating CFD using OpenFOAM with GPUs

Accelerating CFD using OpenFOAM with GPUs Accelerating CFD using OpenFOAM with GPUs Authors: Saeed Iqbal and Kevin Tubbs The OpenFOAM CFD Toolbox is a free, open source CFD software package produced by OpenCFD Ltd. Its user base represents a wide

More information

For designers and engineers, Autodesk Product Design Suite Standard provides a foundational 3D design and drafting solution.

For designers and engineers, Autodesk Product Design Suite Standard provides a foundational 3D design and drafting solution. Autodesk Product Design Suite Standard 2013 System Requirements Typical Persona and Workflow For designers and engineers, Autodesk Product Design Suite Standard provides a foundational 3D design and drafting

More information

Choosing a Computer for Running SLX, P3D, and P5

Choosing a Computer for Running SLX, P3D, and P5 Choosing a Computer for Running SLX, P3D, and P5 This paper is based on my experience purchasing a new laptop in January, 2010. I ll lead you through my selection criteria and point you to some on-line

More information

Das Ising-Modell auf Grafikkarten

Das Ising-Modell auf Grafikkarten Das Ising-Modell auf Grafikkarten Institute of Physics, Johannes Gutenberg-University of Mainz Center for Polymer Studies, Department of Physics, Boston University Artemis Capital Asset Management GmbH

More information

Real-Time Realistic Rendering. Michael Doggett Docent Department of Computer Science Lund university

Real-Time Realistic Rendering. Michael Doggett Docent Department of Computer Science Lund university Real-Time Realistic Rendering Michael Doggett Docent Department of Computer Science Lund university 30-5-2011 Visually realistic goal force[d] us to completely rethink the entire rendering process. Cook

More information

Xbox One v PlayStation 4 Architectures BY: YASSER ALSHALAAN ZACHARY TEASDALE

Xbox One v PlayStation 4 Architectures BY: YASSER ALSHALAAN ZACHARY TEASDALE Xbox One v PlayStation 4 Architectures BY: YASSER ALSHALAAN ZACHARY TEASDALE Overview Background AMD Jaguar Architecture Xbox One Features Architecture PlayStation4 Features Architecture Critical Reception

More information

The Future Of Animation Is Games

The Future Of Animation Is Games The Future Of Animation Is Games 王 銓 彰 Next Media Animation, Media Lab, Director cwang@1-apple.com.tw The Graphics Hardware Revolution ( 繪 圖 硬 體 革 命 ) : GPU-based Graphics Hardware Multi-core (20 Cores

More information

This Unit: Putting It All Together. CIS 501 Computer Architecture. Sources. What is Computer Architecture?

This Unit: Putting It All Together. CIS 501 Computer Architecture. Sources. What is Computer Architecture? This Unit: Putting It All Together CIS 501 Computer Architecture Unit 11: Putting It All Together: Anatomy of the XBox 360 Game Console Slides originally developed by Amir Roth with contributions by Milo

More information

High Performance GPGPU Computer for Embedded Systems

High Performance GPGPU Computer for Embedded Systems High Performance GPGPU Computer for Embedded Systems Author: Dan Mor, Aitech Product Manager September 2015 Contents 1. Introduction... 3 2. Existing Challenges in Modern Embedded Systems... 3 2.1. Not

More information

Go Faster - Preprocessing Using FPGA, CPU, GPU. Dipl.-Ing. (FH) Bjoern Rudde Image Acquisition Development STEMMER IMAGING

Go Faster - Preprocessing Using FPGA, CPU, GPU. Dipl.-Ing. (FH) Bjoern Rudde Image Acquisition Development STEMMER IMAGING Go Faster - Preprocessing Using FPGA, CPU, GPU Dipl.-Ing. (FH) Bjoern Rudde Image Acquisition Development STEMMER IMAGING WHO ARE STEMMER IMAGING? STEMMER IMAGING is: Europe's leading independent provider

More information

GPU-based Decompression for Medical Imaging Applications

GPU-based Decompression for Medical Imaging Applications GPU-based Decompression for Medical Imaging Applications Al Wegener, CTO Samplify Systems 160 Saratoga Ave. Suite 150 Santa Clara, CA 95051 sales@samplify.com (888) LESS-BITS +1 (408) 249-1500 1 Outline

More information

Optimizing a 3D-FWT code in a cluster of CPUs+GPUs

Optimizing a 3D-FWT code in a cluster of CPUs+GPUs Optimizing a 3D-FWT code in a cluster of CPUs+GPUs Gregorio Bernabé Javier Cuenca Domingo Giménez Universidad de Murcia Scientific Computing and Parallel Programming Group XXIX Simposium Nacional de la

More information

Guided Performance Analysis with the NVIDIA Visual Profiler

Guided Performance Analysis with the NVIDIA Visual Profiler Guided Performance Analysis with the NVIDIA Visual Profiler Identifying Performance Opportunities NVIDIA Nsight Eclipse Edition (nsight) NVIDIA Visual Profiler (nvvp) nvprof command-line profiler Guided

More information

Speeding Up RSA Encryption Using GPU Parallelization

Speeding Up RSA Encryption Using GPU Parallelization 2014 Fifth International Conference on Intelligent Systems, Modelling and Simulation Speeding Up RSA Encryption Using GPU Parallelization Chu-Hsing Lin, Jung-Chun Liu, and Cheng-Chieh Li Department of

More information

GPUs for Scientific Computing

GPUs for Scientific Computing GPUs for Scientific Computing p. 1/16 GPUs for Scientific Computing Mike Giles mike.giles@maths.ox.ac.uk Oxford-Man Institute of Quantitative Finance Oxford University Mathematical Institute Oxford e-research

More information

Revision of Relational Joins for Multi-Core And Revision of Relational Joins for Multi-Core and Many-Core Architectures. Many-Core Architectures

Revision of Relational Joins for Multi-Core And Revision of Relational Joins for Multi-Core and Many-Core Architectures. Many-Core Architectures Revision of Relational Joins for Multi-Core And Revision of Relational Joins for Multi-Core and Many-Core Architectures Many-Core Architectures Martin Kruliš, Jakub Yaghob Martin Kruliš and Jakub Yaghob

More information

ST810 Advanced Computing

ST810 Advanced Computing ST810 Advanced Computing Lecture 17: Parallel computing part I Eric B. Laber Hua Zhou Department of Statistics North Carolina State University Mar 13, 2013 Outline computing Hardware computing overview

More information

QuickSpecs. NVIDIA Quadro K420 1GB Graphics INTRODUCTION PERFORMANCE AND FEATURES COMPATIBILITY. NVIDIA Quadro K420 1GB Graphics

QuickSpecs. NVIDIA Quadro K420 1GB Graphics INTRODUCTION PERFORMANCE AND FEATURES COMPATIBILITY. NVIDIA Quadro K420 1GB Graphics J3G86AA INTRODUCTION The NVIDIA Quadro K420 delivers power-efficient 3D application performance and capability. 1 GB of DDR3 GPU memory with fast bandwidth enables you to create complex 3D models, and

More information

PLANNING FOR DENSITY AND PERFORMANCE IN VDI WITH NVIDIA GRID JASON SOUTHERN SENIOR SOLUTIONS ARCHITECT FOR NVIDIA GRID

PLANNING FOR DENSITY AND PERFORMANCE IN VDI WITH NVIDIA GRID JASON SOUTHERN SENIOR SOLUTIONS ARCHITECT FOR NVIDIA GRID PLANNING FOR DENSITY AND PERFORMANCE IN VDI WITH NVIDIA GRID JASON SOUTHERN SENIOR SOLUTIONS ARCHITECT FOR NVIDIA GRID AGENDA Recap on how vgpu works Planning for Performance - Design considerations -

More information

QuickSpecs. NVIDIA Quadro K1200 4GB Graphics INTRODUCTION PERFORMANCE AND FEATURES. Overview

QuickSpecs. NVIDIA Quadro K1200 4GB Graphics INTRODUCTION PERFORMANCE AND FEATURES. Overview Overview L4D16AA INTRODUCTION The NVIDIA Quadro K1200 delivers outstanding professional 3D application performance in a low profile plug-in card form factor. This card is dedicated for small form factor

More information

Autodesk 2014 Product Line System Requirements

Autodesk 2014 Product Line System Requirements Autodesk 2014 Product Line System Requirements Autodesk 2014 Product Line System Requirements... 1 Autodesk Inventor 2014 family of products... 2 Windows Users:... 2 For General Part and Assembly Design

More information

GPU File System Encryption Kartik Kulkarni and Eugene Linkov

GPU File System Encryption Kartik Kulkarni and Eugene Linkov GPU File System Encryption Kartik Kulkarni and Eugene Linkov 5/10/2012 SUMMARY. We implemented a file system that encrypts and decrypts files. The implementation uses the AES algorithm computed through

More information

QuickSpecs. NVIDIA Quadro K5200 8GB Graphics INTRODUCTION. NVIDIA Quadro K5200 8GB Graphics. Technical Specifications

QuickSpecs. NVIDIA Quadro K5200 8GB Graphics INTRODUCTION. NVIDIA Quadro K5200 8GB Graphics. Technical Specifications J3G90AA INTRODUCTION The NVIDIA Quadro K5200 gives you amazing application performance and capability, making it faster and easier to accelerate 3D models, render complex scenes, and simulate large datasets.

More information

Trends in High-Performance Computing for Power Grid Applications

Trends in High-Performance Computing for Power Grid Applications Trends in High-Performance Computing for Power Grid Applications Franz Franchetti ECE, Carnegie Mellon University www.spiral.net Co-Founder, SpiralGen www.spiralgen.com This talk presents my personal views

More information